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FAUN 1.1 User Manual



Today's neurocomputation usually is based on complete software emulation and is therefore often called neurosimulation. Inputs, outputs, neurons, synapses and weights are implemented in software. The neurosimulator FAUN (Fast Approximation with Universal Neural networks) enables supervised learning with 3- and 4-layered perceptrons and also radial basis functions. A FAUN user has to provide patterns, i.e. input-output pairs explaining a mathematical relation. Then artificial neural networks (ANN) are trained to learn the relation with a black-box approach. A well trained ANN reasonably interpolates and extrapolates between the patterns (generalization). This discussion paper shows in detail how FAUN works and gives several examples of use.

Suggested Citation

  • Simon König & Frank Köller & Prof. Dr. Michael H. Breitner, 2005. "FAUN 1.1 User Manual," IWI Discussion Paper Series 16, Institut für Wirtschaftsinformatik, Universität Hannover.
  • Handle: RePEc:ifw:iwidps:iwidps16

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    More about this item


    artificial intelligence; neural network; neurosimulator; neurosimulation; SQP-training method;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques


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